Documentation of the Evaluation of CALPUFF and Other Long ...

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4.4 MODEL PERFORMANCE EVALUATION FOR THE SRL75 TRACER EXPERIMENT The Irwin (1997) plume fitting evaluation approach was used to evaluate CALPUFF for the SRL75 field experiment. There are two components to the Irwin plume fitting evaluation approach: 1. A temporal analysis that examines the time the tracer arrives, leaves and resides on the receptor arc; and 2. A plume fitting procedures that compares the predicted observed peak and average plume concentrations and the width of the plume by fitting a Gaussian plume through the predicted or observed concentrations across the arc of receptors or monitors that lie on the 100 km receptor arc. Because only long‐term integrated average observed SF6 samples were available, the timing component of the evaluation could not be compared against observed values in the SRL75 experiments. Most of the CALPUFF sensitivity tests estimated that the tracer arrived at the 100 km arc on hour 13 LST, 2½ hours after the beginning to the tracer release. The exceptions to this are the CALPUFF/MMIF simulations using the 4 km MM5 data and CALPUFF/MMIF using the 36 km and PG dispersion that estimated the plume arrives at hour 14 LST. With one exception, the CALPUFF simulations estimated that the tracer resided either 5 or 6 hours on the arc. And with two exceptions, it was the meteorological data rather than the dispersion option that defined the residence time of the estimated tracer on the 100 km receptor arc. The exceptions were for the PG dispersion sensitivity test that in two cases predicted the tracer would remain one less hour on the arc; the CALPUFF/CALMET BASE sensitivity test using the PG dispersion estimated that the tracer would reside only 4 hours on the 100 km receptor arc. Without any observed tracer timing statistics, these results are difficult to interpret. Table 4‐9 displays the model performance evaluation for the various CALPUFF sensitivity tests using the Irwin plume fitting evaluation approach. The observed values were taken from the 1998 EPA CALPUFF tracer test evaluation report data (EPA, 1998a). Also shown in Table 4‐4 are the statistics from the 1998 EPA report for the CALPUFF V4.0 modeling using Pasquill‐Gifford (PG) and similarity (CAL) dispersion. Note that the EPA 1998 CALPUFF modeling used CALMET with just observations so is analogous to the BASE sensitivity scenario that used CALPUFF V5.8. There are five statistical parameters evaluated using the Irwin plume fitting evaluation approach: • Cmax, which is the plume fitted centerline concentration. • Omax, which is the maximum observed value at the ~40 monitoring sites or maximum predicted value across the ~200 receptors along the 100 km arc. • Sigma‐y, which the second moment of the Gaussian distribution and a measure of the plume spread. • Plume Centerline, which is the angle of the plume centerline from the source to the 100 km arc. • CWIC, the cross wind integrated concentration (CWIC) across the predicted and observed fitted Gaussian plume. The first thing we note in Table 4‐9 is that the maximum centerline concentration of the fitted Gaussian plume to the observed SF6 tracer concentrations across the 12 monitors (2.739 ppt) is almost half the observed maximum at any of the monitors (5.07 ppt). As the centerline concentrations in a Gaussian plume represents the maximum concentration, this means that 63
the fitted Gaussian plume is not a very good fit of the observations and the Cmax parameter is not a good indicator of model performance. Comparison of the predicted and observed Omax values that represents the maximum observed concentration across the monitoring sites and the maximum predicted value at any of the 200 receptors along the arc is an apple‐orange comparison. We would expect the predicted Omax value to be the same or larger than the observed Omax value given there are ~5 times more samples of the plume in the model predictions compared to the observations. This is the case for all of the CALPUFF/MMIF sensitivity tests. However, when CALPUFF is run using CALMET with no MM5 data (BASE), the predicted Omax value is less than the observed value for both CALPUFF V4.0 and CALPUFF V5.8, which is an undesirable attribute. The fitted plume width (sigma‐y) based on observations is almost doubled the fitted plume width based on the CALPUFF model predictions for all the CALPUFF simulations. However, this is likely due in part to the poor Gaussian plume fit of the observations. Figure 4‐2 is reproduced from the 1998 EPA CALPUFF tracer test report and compares the CALPUFF fitted Gaussian plume concentrations with the 13 observed tracer concentrations, where the predicted and observed tracer distributions have been rotated so that their centerlines match up. Of the 13 monitors pictured along the 100 km arc, four have substantial (> 2.0 ppt) concentrations whereas the tracer concentrations at the remaining monitoring sites are mostly
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Documentation of the Evaluation of
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FOREWARD This report documents the
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2.4.3 ATMES‐II Model Evaluation A
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EXECUTIVE SUMMARY ABSTRACT The CALP
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OVERVIEW OF APPROACH Up to six LRT
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CALPUFF performance is evaluated by
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Table ES‐2. ATMES‐II spatial an
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• CALPUFF tended to overstate the
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• The best performing CALPUFF con
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2009a). The key findings from the C
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1.8 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2
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2.4 2 1.6 1.2 0.8 0.4 0 EXP4A EXP4B
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Figure ESS‐4. RANK statistical pe
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Evaluatioon of Six LRT T Dispersion
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Table ES‐6. Summary of model rank
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eproduce the northwest to southeast
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ETEX LRT Dispersion Model Sensitivi
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CONCLUSIONS OF LRT DISPERSION MODEL
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The CAMx and CALGRID Eulerian photo
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July 1980. Both experiments examine
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1.3 ORGANIZATION OF REPORT Chapter
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puffs expand until they exceed the
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that performance evaluation be base
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The ETEX real‐time LRT modeling p
Page 49 and 50: The ETEX study has formulated the f
Page 51 and 52: In this study we expand the LRT mod
Page 53 and 54: AM ∩ AP FMS = × 100% (2‐2) A
Page 55 and 56: Factor of α (FAα): FAα represent
Page 57 and 58: 3.0 1980 GREAT PLAINS FIELD STUDY 3
Page 59 and 60: compact discs, which were used to o
Page 61 and 62: ILEVZI = 1 Layer of winds to use in
Page 63 and 64: MCHEM = 0 No chemical transformatio
Page 65 and 66: Table 3‐6. CALPUFF/CALMET experim
Page 67 and 68: Table 3‐11. CALPUFF/MMIF sensitiv
Page 69 and 70: evaluation studies and evaluate whe
Page 71 and 72: Tables 3‐13 and Figures 3‐2 thr
Page 73 and 74: 140% 120% 100% 80% 60% 40% 20% 0%
Page 75 and 76: 30% 20% 10% 0% ‐10% ‐20% ‐30%
Page 77 and 78: 120% 100% 80% 60% 40% 20% 0% ‐20%
Page 79 and 80: 20% 10% 0% ‐10% ‐20% ‐30% ‐
Page 81 and 82: The fitted Gaussian plume statistic
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Page 85 and 86: 300% 250% 200% 150% 100% 50% 0% 300
Page 87 and 88: 60% 40% 20% 0% ‐20% ‐40% ‐60%
Page 89 and 90: with APS, implementing the slug opt
Page 91 and 92: the amount of time that the tracer
Page 93 and 94: Figure 4‐1. CALPUFF/CALMET UTM mo
Page 95 and 96: compact discs, which were used to o
Page 97 and 98: Table 4‐4. CALPUFF parameters use
Page 99: Table 4‐8. CALPUFF/MMIF sensitivi
Page 103 and 104: Figure 4‐2. Comparison of predict
Page 105 and 106: Figure 5‐1. Location of Dayton an
Page 107 and 108: MM5 runs, the first without FDDA (i
Page 109 and 110: Table 5‐3. MM5 sensitivity tests
Page 111 and 112: Table 5‐6. Definition of the CALM
Page 113 and 114: performance at the monitor location
Page 115 and 116: 35% 30% 25% 20% 15% 10% 5% 0% 35% 3
Page 117 and 118: 40% 35% 30% 25% 20% 15% 10% 5% 0% F
Page 119 and 120: 40% 35% 30% 25% 20% 15% 10% 5% 0% E
Page 121 and 122: 5.4.1.4 Comparison of CALPUFF CTEX3
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Page 125 and 126: CTEX3 discussed in Section 5.4.1. A
Page 127 and 128: CALPUFF sensitivity simulations are
Page 129 and 130: 14. Across all the spatial statisti
Page 131 and 132: sensitivity tests. The “B” seri
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Page 135 and 136: 6.0 1994 EUROPEAN TRACER EXPERIMENT
Page 137 and 138: Figure 6‐2a. Surface synoptic met
Page 139 and 140: Figure 6‐3a. Distribution of the
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Figure 6‐9. Factor of Exceedance
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eceiving a 0.0 score. Figure 6‐13
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Table 6‐1. Summary of model ranki
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plume spread and observed surface c
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Figure 6‐16c. Comparison of spati
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• NoPiG: The tracer emissions wer
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Using the NMSE statistical performa
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6.4.3.2 Effect of PiG on Model Perf
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80 70 60 50 40 30 20 10 0 1 2 3 4 5
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Table 6‐3. Summary of CALPUFF puf
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0.2 0.18 0.16 0.14 0.12 0.1 0.08 0.
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Figure 6‐ ‐22 displays the t sp
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Figure 6‐ ‐23a. Global model pe
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Figure 6‐24. Figure of Merit (FMS
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7.0 REFERENCES Anderson, B. 2008. T
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EPA, 1984: Interim Procedures for E
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Mlawer, E.J., S.J. Taubman, P.D. Br
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148 Appendix A Evaluation of the MM
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Table A‐1. Wind speed and wind di
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Table A‐3. Definition of the CTEX
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Figure A‐ ‐1. Wind speed bias (
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Figure A‐ ‐3. Humidity bias and
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Table A‐5. Comparison of CTEX5 MM
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B.1 CALMET MODEL EVALUATION TO IDEN
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Figure B‐ ‐1 displays th he win
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Table B‐2a. Summary wind speed mo
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B.2 CONCLUSIONS OF CTEX3 CALMET SEN
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C.1 INTRODUCTION In this section, t
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C.2.2 HYYSPLIT GLOB BAL STATISTIICS
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The final panel in Figure C‐3 (bo
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Figure C‐ ‐5. Global model m pe
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C.3 CAMX SENSITIVITY TESTS Followin
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ACM2 Kzz combinatio ons rank as the
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Figure C‐ ‐10. Spatial model pe
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Figure C‐ ‐12. Global model per
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Figure C‐ ‐13. Spatial model pe
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Table C‐3. CAMx FMS and POD spati
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Figure C‐ ‐20. False Alarm Rate
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Figure C‐ ‐23. Factor of o Exce
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The PCC values for th he six LRT mo
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The RANK statistical performancce m
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Table C‐55. Summary y of model r
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Results foor the TS me etric are pr
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Figure C‐ ‐35. Factor of o 2 (F
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a negativve FB. The best b performm
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performing model the most often sco
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United States Environmental Protect
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